wifi-densepose/examples/scipix/docs/15_ROADMAP.md

Ruvector-Scipix Implementation Roadmap

Version: 1.0.0 Date: 2025-11-28 Project: ruvector-scipix Methodology: SPARC (Specification, Pseudocode, Architecture, Refinement, Completion)

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Table of Contents

1. Executive Summary
2. Phase 0: Foundation
3. Phase 1: Specification (SPARC-S)
4. Phase 2: Pseudocode (SPARC-P)
5. Phase 3: Architecture (SPARC-A)
6. Phase 4: Refinement (SPARC-R)
7. Phase 5: Completion (SPARC-C)
8. Milestone Definitions
9. Dependencies and Risks
10. Success Metrics
11. Timeline Overview

---

Executive Summary

This roadmap outlines the implementation plan for ruvector-scipix, a high-performance Rust-based OCR system specialized for mathematical content extraction. The project follows the SPARC methodology with 6 phases spanning 16-20 weeks from foundation to production release.

Key Goals:

• 95%+ accuracy on printed mathematical expressions
• <100ms latency for single image processing
• Full Scipix API v3 compatibility
• Production-ready Rust implementation

Total Estimated Duration: 16-20 weeks Team Size: 2-4 developers Critical Path: Model training → Core OCR engine → API implementation

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Phase 0: Foundation

Duration: 2 weeks Status: ✅ Complete Objective: Establish project infrastructure and development environment

Deliverables

0.1 Project Setup

• Create workspace structure in examples/scipix/
• Initialize crate structure (core, models, api, cli, wasm)
• Set up Cargo.toml with dependencies
• Configure development tools (rustfmt, clippy)
• Create .gitignore and .editorconfig

Location: /home/user/ruvector/examples/scipix/

0.2 CI/CD Pipeline

• GitHub Actions workflow for Rust builds
• Add test automation (unit + integration)
• Set up code coverage tracking (codecov)
• Configure release automation
• Set up Docker build pipeline

Files:

.github/workflows/
├── ci.yml           # Build and test
├── benchmarks.yml   # Performance benchmarks
└── release.yml      # Release automation

0.3 Documentation Framework

• Create docs/ directory structure
• Write initial README.md
• Set up API documentation (rustdoc)
• Create contributing guidelines
• Write code of conduct

Documentation Structure:

docs/
├── 01_SPECIFICATION.md       ✅
├── 04_ARCHITECTURE.md        ✅
├── 05_PSEUDOCODE.md          ✅
├── 06_LATEX_PIPELINE.md      ✅
├── 07_IMAGE_PREPROCESSING.md ✅
├── 08_BENCHMARKS.md          ✅
├── 09_OPTIMIZATION.md        ✅
├── 10_LEAN_AGENTIC.md        ✅
└── 15_ROADMAP.md             ← Current document

0.4 Development Environment

• Set up local development Docker environment
• Configure IDE settings (VSCode/RustRover)
• Install ONNX Runtime dependencies
• Set up GPU development environment (optional)
• Configure debugging tools (lldb, gdb)

Dependencies:

# Ubuntu/Debian
apt-get install build-essential pkg-config libssl-dev cmake

# macOS
brew install cmake openssl

# Rust toolchain
rustc >= 1.77.0
cargo >= 1.77.0

0.5 Test Data Preparation

• Download Im2latex-100k dataset
• Download CROHME dataset
• Create custom test set (100 samples)
• Organize test data structure
• Create ground truth annotations

Data Structure:

testdata/
├── im2latex-100k/
│   ├── images/
│   └── formulas.txt
├── crohme/
│   └── CROHME2019/
├── custom/
│   ├── easy/
│   ├── medium/
│   ├── hard/
│   └── ground_truth.json
└── benchmarks/
    └── performance_test_set/

Success Criteria

• ✅ All crates compile successfully
• ✅ CI/CD pipeline executes without errors
• ✅ Documentation structure in place
• Development environment fully configured
• Test datasets downloaded and organized

---

Phase 1: Specification (SPARC-S)

Duration: 2 weeks Status: ✅ Complete Objective: Finalize requirements and API specifications

Deliverables

1.1 Requirements Finalization

• Complete functional requirements document
• Define non-functional requirements (performance, scalability)
• Document API compatibility requirements
• Create use case scenarios
• Define acceptance criteria

Document: docs/01_SPECIFICATION.md (Complete)

1.2 API Specification

• Define REST API endpoints (/v3/text, /v3/strokes, /v3/pdf, /v3/latex)
• Specify request/response formats
• Document authentication mechanism
• Define rate limiting strategy
• Create OpenAPI 3.0 specification

Deliverable:

# config/openapi.yaml
openapi: 3.0.0
info:
  title: Ruvector-Scipix API
  version: 0.1.0
paths:
  /v3/text:
    post: ...
  /v3/strokes:
    post: ...
  /v3/pdf:
    post: ...
  /v3/latex:
    post: ...

1.3 Data Model Design

• Define core data structures (MathExpression, Symbol, etc.)
• Specify input/output types
• Design error handling hierarchy
• Document serialization formats
• Create schema validation rules

Key Types:

// Defined in specification
- ImageInput
- MathExpression
- RecognitionResponse
- ErrorResponse
- BatchResponse

1.4 Test Plan Creation

• Define test coverage targets (80%+ unit, 70%+ integration)
• Create test case templates
• Specify benchmark requirements
• Document acceptance test procedures
• Create performance test scenarios

Test Plan Structure:

tests/
├── unit/           # 80%+ coverage target
├── integration/    # 70%+ coverage target
├── e2e/            # Critical path scenarios
└── benchmarks/     # Performance regression tests

1.5 Security and Compliance Review

• Document security requirements (auth, rate limiting, input validation)
• Identify compliance needs (GDPR, data privacy)
• Define threat model
• Create security testing plan
• Document audit requirements

Success Criteria

• ✅ Requirements document approved by stakeholders
• ✅ API specification matches Scipix API v3 (95%+ compatible)
• ✅ Data models validated and documented
• Test plan covers all critical paths
• Security review completed

---

Phase 2: Pseudocode (SPARC-P)

Duration: 2 weeks Status: ✅ Complete Objective: Design algorithms and processing pipelines

Deliverables

2.1 Algorithm Design

• Image preprocessing algorithms (deskew, denoise, enhance)
• Text detection algorithm (EAST/CRAFT-based)
• Character recognition algorithm (CRNN)
• Math structure parsing algorithm
• LaTeX generation algorithm

Document: docs/05_PSEUDOCODE.md (Complete)

2.2 Pipeline Architecture

• Design end-to-end processing pipeline
• Define pipeline stages and transitions
• Specify parallelization strategy
• Document error handling flow
• Create pipeline configuration options

Pipeline Stages:

1. Image Loading & Validation
2. Preprocessing (normalize, denoise, deskew)
3. Vector Cache Lookup (similarity search)
4. Text Detection (region identification)
5. OCR Recognition (parallel processing)
6. Math Parsing (structure analysis)
7. Output Formatting (LaTeX, MathML, etc.)
8. Cache Update (store embeddings)

2.3 Interface Definitions

• Define trait interfaces for extensibility
• Specify plugin system interfaces
• Document model loader interface
• Create cache interface abstractions
• Define formatter plugin interface

Key Traits:

pub trait PipelineStage: Send + Sync {
    async fn execute(&self, context: &mut PipelineContext) -> Result<()>;
}

pub trait RecognitionModel: Send + Sync {
    fn recognize(&self, image: &Image) -> Result<Recognition>;
}

pub trait OutputFormatter: Send + Sync {
    fn format(&self, expr: &MathExpression) -> Result<String>;
}

2.4 Performance Targets

• Define latency targets (p50/p95/p99)
• Specify throughput requirements
• Document memory usage limits
• Set accuracy targets (CER, WER, BLEU)
• Create performance regression thresholds

Targets:

Latency:
- P50: <50ms
- P95: <100ms
- P99: <200ms

Throughput:
- Single-threaded: 100 req/s
- 4 cores: 350+ req/s
- 8 cores: 650+ req/s

Accuracy:
- Printed math: 95%+ CER
- Handwritten: 90%+ CER
- Chemical formulas: 93%+ accuracy

2.5 Optimization Strategy

• Identify optimization opportunities
• Design caching strategy (vector + result caching)
• Plan parallel processing approach
• Document GPU acceleration opportunities
• Create memory optimization plan

Success Criteria

• ✅ All core algorithms designed and documented
• ✅ Pipeline architecture validated for performance targets
• ✅ Interfaces defined for extensibility
• ✅ Performance targets established and realistic
• ✅ Optimization strategy documented

---

Phase 3: Architecture (SPARC-A)

Duration: 3 weeks Status: ✅ Complete Objective: Implement crate structure and core trait definitions

Deliverables

3.1 Crate Structure Implementation

• Create ruvector-scipix-core crate
• Create ruvector-scipix-models crate
• Create ruvector-scipix-api crate
• Create ruvector-scipix-cli crate
• Create ruvector-scipix-wasm crate

Workspace Structure:

[workspace]
members = [
    "crates/ruvector-scipix-core",
    "crates/ruvector-scipix-models",
    "crates/ruvector-scipix-api",
    "crates/ruvector-scipix-cli",
    "crates/ruvector-scipix-wasm",
]

3.2 Core Trait Definitions

• Implement PipelineStage trait
• Implement RecognitionModel trait
• Implement PreprocessorPlugin trait
• Implement FormatterPlugin trait
• Implement CachePlugin trait

Files:

crates/ruvector-scipix-core/src/
├── lib.rs
├── traits/
│   ├── mod.rs
│   ├── pipeline.rs
│   ├── model.rs
│   └── plugin.rs
└── types.rs

3.3 Error Handling Framework

• Define error type hierarchy (ScipixError)
• Implement error recovery strategies
• Create error context propagation
• Add error logging integration
• Document error handling patterns

Error Types:

pub enum ScipixError {
    InvalidImageFormat(String),
    PreprocessingError(String),
    ImageTooLarge { size: u64, max: u64 },
    ModelNotFound(String),
    ModelLoadError(String),
    InferenceError(String),
    DetectionError(String),
    RecognitionError(String),
    ParseError(String),
    CacheError(String),
    // ... (see specification)
}

3.4 Configuration System

• Create configuration structure (ScipixConfig)
• Implement TOML/JSON loading
• Add environment variable overrides
• Create validation logic
• Document configuration options

Configuration Files:

config/
├── default.toml      # Default configuration
├── development.toml  # Dev environment
├── production.toml   # Production settings
└── test.toml         # Testing configuration

3.5 Logging and Tracing

• Integrate tracing crate
• Set up structured logging
• Configure log levels
• Add span instrumentation
• Create logging guidelines

Tracing Setup:

use tracing::{info, debug, instrument};

#[instrument(skip(image_data))]
async fn process_image(image_data: &[u8]) -> Result<Recognition> {
    info!("Starting image processing");
    // ...
}

Success Criteria

• All crates compile and pass clippy
• Core traits documented and tested
• Error handling covers all failure modes
• Configuration system functional
• Logging integrated throughout codebase

---

Phase 4: Refinement (SPARC-R)

Duration: 6 weeks Status: 🚧 In Progress Objective: TDD implementation of core functionality

Deliverables

4.1 TDD Cycle 1: Image Preprocessing (Week 1)

• Write tests for image loading
• Implement image format detection
• Write tests for normalization
• Implement normalization algorithm
• Write tests for denoising
• Implement bilateral filter
• Write tests for deskewing
• Implement rotation correction
• Write tests for binarization
• Implement adaptive thresholding

Test Coverage Target: 85%+

Implementation:

// crates/ruvector-scipix-core/src/preprocess/
mod normalize;
mod denoise;
mod deskew;
mod enhance;
mod segment;

// tests/
#[test]
fn test_image_normalization() { ... }
#[test]
fn test_denoising() { ... }

4.2 TDD Cycle 2: Model Integration (Week 2)

• Write tests for model loading
• Implement ONNX model loader
• Write tests for model cache
• Implement model pool
• Write tests for inference
• Implement inference engine
• Write tests for batch processing
• Implement batch inference
• Benchmark model loading time
• Optimize memory usage

Test Coverage Target: 80%+

Models to Integrate:

models/
├── craft_mlt_25k.onnx          # Text detection
├── crnn_vgg_bilstm_ctc.onnx    # Character recognition
└── math_symbol_classifier.onnx # Symbol classification

4.3 TDD Cycle 3: OCR Engine (Week 3)

• Write tests for text detection
• Implement EAST/CRAFT detector
• Write tests for region extraction
• Implement bounding box extraction
• Write tests for character recognition
• Implement CRNN recognizer
• Write tests for confidence scoring
• Implement confidence calculation
• Benchmark detection performance
• Optimize parallel processing

Test Coverage Target: 85%+

Performance Target:

• Detection: <50ms
• Recognition: <100ms
• Total OCR: <150ms

4.4 TDD Cycle 4: Math Parser (Week 4)

• Write tests for symbol recognition
• Implement symbol classifier
• Write tests for layout analysis
• Implement spatial analysis
• Write tests for structure detection
• Implement structure classifier
• Write tests for expression tree building
• Implement tree builder
• Test on complex expressions
• Validate against ground truth

Test Coverage Target: 80%+

Test Cases:

• Simple expressions: x^2 + 2x + 1
• Fractions: \frac{a}{b}
• Matrices: \begin{bmatrix}...\end{bmatrix}
• Integrals: \int_{0}^{\infty} e^{-x} dx

4.5 TDD Cycle 5: Output Formatting (Week 5)

• Write tests for LaTeX generation
• Implement LaTeX formatter
• Write tests for MathML generation
• Implement MathML formatter
• Write tests for MMD generation
• Implement MMD formatter
• Write tests for HTML output
• Implement HTML formatter
• Validate output correctness
• Test rendering compatibility

Test Coverage Target: 85%+

Output Formats:

• LaTeX (primary)
• MathML (presentation + content)
• Scipix Markdown (MMD)
• ASCII art (basic)
• HTML with MathJax

4.6 TDD Cycle 6: Vector Cache Integration (Week 6)

• Write tests for embedding generation
• Implement image embedding model
• Write tests for similarity search
• Implement HNSW vector search
• Write tests for cache updates
• Implement cache storage
• Write tests for cache hits
• Benchmark cache performance
• Test cache eviction policy
• Optimize memory usage

Test Coverage Target: 80%+

Cache Metrics:

• Hit rate target: >80%
• Search latency: <10ms
• Insert latency: <5ms

Success Criteria

• All core modules implemented with TDD
• Test coverage exceeds 80% overall
• All unit tests passing
• Integration tests covering critical paths
• Performance targets met or exceeded
• Code review completed

---

Phase 5: Completion (SPARC-C)

Duration: 3 weeks Status: ⏳ Pending Objective: Integration, documentation, and release preparation

Deliverables

5.1 Integration Testing (Week 1)

• Write end-to-end test suite
• Test complete pipeline
• Validate API compatibility
• Test error handling paths
• Benchmark full system
• Load testing (concurrent users)
• Stress testing (resource limits)
• Chaos testing (failure injection)

Test Scenarios:

E2E Tests:
1. Simple equation recognition
2. Complex mathematical expression
3. PDF processing (multi-page)
4. Batch processing (100 images)
5. Handwritten math
6. Chemical formulas
7. Error recovery
8. API authentication

Performance Benchmarks:

• Run Im2latex-100k benchmark
• Run CROHME benchmark
• Compare with Scipix API
• Generate performance report

5.2 API Server Development (Week 2)

• Implement REST endpoints
• Add authentication middleware
• Implement rate limiting
• Add request validation
• Create WebSocket support
• Add health check endpoints
• Implement metrics collection
• Set up API documentation

API Endpoints:

POST /v3/text      # Image → LaTeX
POST /v3/strokes   # Handwriting → LaTeX
POST /v3/pdf       # PDF → LaTeX
POST /v3/latex     # LaTeX → PNG/SVG
GET  /health       # Health check
GET  /metrics      # Prometheus metrics

5.3 CLI Tool Development (Week 2)

• Implement command-line parser
• Add image processing command
• Add batch processing command
• Add format conversion command
• Create interactive mode
• Add progress indicators
• Implement output formatting
• Write CLI documentation

CLI Commands:

ruvector-scipix ocr image.png              # Single image
ruvector-scipix batch images/*.png         # Batch processing
ruvector-scipix convert latex "x^2+1"      # LaTeX rendering
ruvector-scipix models list                # Model management

5.4 Documentation (Week 3)

• Complete API reference documentation
• Write user guide
• Create tutorial examples
• Document deployment procedures
• Write troubleshooting guide
• Create FAQ
• Generate rustdoc
• Publish documentation site

Documentation Structure:

docs/
├── user-guide/
│   ├── installation.md
│   ├── quickstart.md
│   ├── api-reference.md
│   └── examples.md
├── developer-guide/
│   ├── architecture.md
│   ├── contributing.md
│   └── plugin-development.md
└── deployment/
    ├── docker.md
    ├── kubernetes.md
    └── configuration.md

5.5 Performance Optimization (Week 3)

• Profile hot paths
• Optimize memory allocations
• Parallelize batch processing
• Implement GPU acceleration (optional)
• Optimize model loading
• Tune cache parameters
• Run final benchmarks
• Generate performance report

Optimization Targets:

Before → After:
- Latency P95: 150ms → <100ms
- Throughput: 50 req/s → 100+ req/s
- Memory: 1GB → <500MB
- Model loading: 5s → <3s

5.6 Release Preparation (Week 3)

• Create release checklist
• Tag version 0.1.0
• Build release binaries
• Create Docker images
• Publish to crates.io
• Publish to Docker Hub
• Create GitHub release
• Announce release

Release Artifacts:

Binaries:
- ruvector-scipix-cli (Linux, macOS, Windows)
- ruvector-scipix-api (Docker image)

Packages:
- ruvector-scipix-core (crates.io)
- ruvector-scipix-wasm (npm)

Documentation:
- User guide
- API reference
- Examples

Success Criteria

• All integration tests passing
• API server functional and tested
• CLI tool working on all platforms
• Documentation complete and reviewed
• Performance targets achieved
• Release artifacts built and published

---

Milestone Definitions

M1: Basic Image → Text

Target Date: Week 6 Deliverables:

• Image loading and preprocessing working
• Basic OCR (text-only) functional
• Simple expressions recognized (x^2, a+b)
• 70%+ accuracy on simple test set

Exit Criteria:

• ✅ Processes PNG/JPEG images
• ✅ Outputs plain text
• ✅ Handles basic errors gracefully
• ✅ Passes 50+ unit tests

M2: Math Expression → LaTeX

Target Date: Week 9 Deliverables:

• Math structure parsing implemented
• LaTeX generation functional
• Symbol recognition accurate
• 80%+ accuracy on medium complexity

Exit Criteria:

• ✅ Recognizes fractions, exponents, subscripts
• ✅ Generates valid LaTeX
• ✅ Handles matrices and complex structures
• ✅ Passes 100+ unit tests

M3: Full API Compatibility

Target Date: Week 12 Deliverables:

• REST API server functional
• All /v3/* endpoints implemented
• Authentication and rate limiting working
• 90%+ Scipix API compatibility

Exit Criteria:

• ✅ POST /v3/text working
• ✅ POST /v3/strokes working
• ✅ POST /v3/pdf working
• ✅ POST /v3/latex working
• ✅ API tests passing

M4: PDF Processing

Target Date: Week 14 Deliverables:

• PDF parsing implemented
• Multi-page processing working
• Layout preservation functional
• Batch processing optimized

Exit Criteria:

• ✅ Processes multi-page PDFs
• ✅ Extracts mathematical content
• ✅ Preserves document structure
• ✅ Handles 100-page documents

M5: Production Ready

Target Date: Week 16 Deliverables:

• Performance targets achieved
• Documentation complete
• Release artifacts built
• Production deployment tested

Exit Criteria:

• ✅ P95 latency <100ms
• ✅ 95%+ accuracy on Im2latex-100k
• ✅ All tests passing
• ✅ Documentation published
• ✅ v0.1.0 released

---

Dependencies and Risks

Critical Dependencies

1. Model Availability

Dependency: Pre-trained ONNX models for OCR Impact: High Mitigation:

• Use publicly available models (CRAFT, CRNN)
• Train custom models if needed
• Have fallback to Tesseract for text-only

Status: ⚠️ Medium risk - models exist but may need fine-tuning

2. ruvector-core Integration

Dependency: Stable ruvector-core API Impact: Medium Mitigation:

• Use stable v0.3.x releases
• Pin dependency versions
• Abstract ruvector interface

Status: ✅ Low risk - ruvector-core is stable

3. Dataset Access

Dependency: Im2latex-100k, CROHME datasets Impact: Medium Mitigation:

• Download and mirror datasets
• Create synthetic test data
• Use alternative datasets

Status: ✅ Low risk - datasets publicly available

4. GPU Support (Optional)

Dependency: CUDA/cuDNN for GPU acceleration Impact: Low Mitigation:

• Make GPU optional feature
• Ensure CPU path works well
• Use cloud GPU for testing

Status: ✅ Low risk - CPU implementation sufficient

Technical Risks

1. Performance Targets

Risk: May not achieve <100ms P95 latency Probability: Medium Impact: High Mitigation:

• Profile early and often
• Optimize critical paths
• Use parallel processing
• Consider GPU acceleration

Contingency: Adjust targets to <200ms if needed

2. Accuracy Requirements

Risk: May not reach 95% accuracy on complex expressions Probability: Medium Impact: High Mitigation:

• Use ensemble models
• Fine-tune on math-specific data
• Implement post-processing corrections
• Provide confidence scores

Contingency: Focus on common use cases, document limitations

3. API Compatibility

Risk: Scipix API may change or have undocumented behavior Probability: Low Impact: Medium Mitigation:

• Document differences clearly
• Version API separately
• Support multiple versions
• Monitor Scipix changes

Contingency: Define "ruvector-scipix extensions"

4. Model Size and Memory

Risk: Models may be too large for deployment constraints Probability: Low Impact: Medium Mitigation:

• Use model quantization (INT8)
• Implement model pruning
• Use memory-mapped files
• Support model sharding

Contingency: Provide "lite" and "full" model variants

Resource Risks

1. Development Team Size

Risk: Limited team capacity (2-4 developers) Probability: High Impact: Medium Mitigation:

• Prioritize critical features
• Use existing libraries
• Automate testing
• Clear documentation

Contingency: Extend timeline by 2-4 weeks if needed

2. Infrastructure Costs

Risk: Training/testing may require expensive GPU resources Probability: Medium Impact: Low Mitigation:

• Use pre-trained models
• Optimize locally first
• Use cloud credits
• Share resources

Contingency: Focus on CPU optimization

3. External Service Dependencies

Risk: Scipix API changes or becomes unavailable for testing Probability: Low Impact: Low Mitigation:

• Cache test results
• Use mock services
• Document baseline behavior
• Create test fixtures

Contingency: Use cached baseline data

Schedule Risks

1. Integration Complexity

Risk: Integration takes longer than expected Probability: Medium Impact: Medium Mitigation:

• Continuous integration
• Early integration testing
• Modular design
• Clear interfaces

Buffer: +1 week in Phase 5

2. Testing and Debugging

Risk: Bugs and edge cases extend testing phase Probability: High Impact: Medium Mitigation:

• TDD from start
• Automated testing
• Regular code reviews
• Bug triage process

Buffer: +1 week in Phase 4

---

Success Metrics

Performance Metrics

Latency Targets

Single Image Processing:
✅ P50: <50ms
✅ P95: <100ms
✅ P99: <200ms

Batch Processing (10 images):
✅ P50: <1000ms
✅ P95: <2000ms

PDF Processing (10 pages):
✅ P50: <5000ms

Measurement: Criterion.rs benchmarks

Throughput Targets

Single-threaded:
✅ 100+ requests/second

4-core system:
✅ 350+ requests/second

8-core system:
✅ 650+ requests/second

Measurement: Load testing with wrk/k6

Resource Usage

Memory:
✅ <500MB base usage
✅ <100MB per concurrent request
✅ <2GB total (100 concurrent)

CPU:
✅ <80% average utilization
✅ Scales linearly with cores

Measurement: System profiling, heaptrack

Accuracy Metrics

Character Error Rate (CER)

Printed Math:
✅ <2% CER on Im2latex-100k
✅ <5% CER on complex expressions

Handwritten Math:
✅ <10% CER on CROHME dataset

Measurement: Levenshtein distance on test sets

Expression Recognition Rate (ERR)

Simple expressions: ✅ 98%+
Fractions: ✅ 95%+
Matrices: ✅ 90%+
Complex (integrals, sums): ✅ 85%+

Measurement: Exact match on ground truth

BLEU Score

LaTeX output quality:
✅ BLEU >85 on test set

Measurement: BLEU-4 on generated LaTeX

Quality Metrics

Code Coverage

Unit tests: ✅ 80%+
Integration tests: ✅ 70%+
Overall: ✅ 75%+

Measurement: tarpaulin, codecov

Code Quality

Clippy warnings: ✅ 0
Rustfmt compliance: ✅ 100%
Documentation: ✅ All public APIs
Security audit: ✅ No critical issues

Measurement: CI checks, cargo audit

API Compatibility

Scipix API v3 compatibility: ✅ 95%+
Endpoint coverage: ✅ 100%
Response format match: ✅ 98%+

Measurement: Compatibility test suite

Business Metrics

Cost Efficiency

Cost per image vs. Scipix:
✅ 10x reduction (self-hosted)

Calculation: Infrastructure costs / images processed

User Adoption

GitHub stars: 🎯 100+ (first month)
Crate downloads: 🎯 1000+ (first 3 months)
Active deployments: 🎯 10+ (first 6 months)

Tracking: GitHub analytics, crates.io stats

Community Engagement

Contributors: 🎯 5+ (first 6 months)
Issues/PRs: 🎯 Responded within 48 hours
Documentation quality: 🎯 <5% unclear feedback

Tracking: GitHub metrics, user feedback

---

Timeline Overview

Gantt Chart (16-Week Plan)

Phase 0: Foundation [Weeks 1-2]
██████ Complete

Phase 1: Specification [Weeks 3-4]
      ██████ Complete

Phase 2: Pseudocode [Weeks 5-6]
            ██████ Complete

Phase 3: Architecture [Weeks 7-9]
                  ██████████ In Progress
                  │ Crate structure ✅
                  │ Trait definitions ⏳
                  └ Configuration ⏳

Phase 4: Refinement [Weeks 10-15]
                        ████████████████████ Planned
                        │ TDD Cycle 1: Preprocessing
                        │ TDD Cycle 2: Models
                        │ TDD Cycle 3: OCR
                        │ TDD Cycle 4: Parser
                        │ TDD Cycle 5: Formatting
                        └ TDD Cycle 6: Caching

Phase 5: Completion [Weeks 16-18]
                                          ████████ Planned
                                          │ Integration
                                          │ API Server
                                          │ Documentation
                                          └ Release

Milestones:
    M1 ▼      M2 ▼     M3 ▼       M4 ▼        M5 ▼
   Week 6   Week 9   Week 12    Week 14    Week 16

Critical Path

1. Model Integration (Week 10) → Must complete before OCR
2. OCR Engine (Week 11) → Blocks Parser
3. Math Parser (Week 12) → Blocks Formatting
4. API Server (Week 16) → Blocks Release
5. Documentation (Week 17) → Blocks Release

Float Time: 2 weeks built into schedule

Weekly Breakdown (Weeks 10-18)

Week 10: Image Preprocessing (TDD Cycle 1)

• Mon-Tue: Image loading and validation
• Wed-Thu: Normalization and denoising
• Fri: Deskewing and binarization

Week 11: Model Integration (TDD Cycle 2)

• Mon-Tue: Model loader and cache
• Wed-Thu: Inference engine
• Fri: Batch processing optimization

Week 12: OCR Engine (TDD Cycle 3)

• Mon-Tue: Text detection
• Wed-Thu: Character recognition
• Fri: Confidence scoring and optimization

Week 13: Math Parser (TDD Cycle 4)

• Mon-Tue: Symbol recognition
• Wed-Thu: Layout analysis
• Fri: Expression tree building

Week 14: Output Formatting (TDD Cycle 5)

• Mon-Tue: LaTeX generation
• Wed: MathML and MMD
• Thu-Fri: HTML output and validation

Week 15: Vector Cache (TDD Cycle 6)

• Mon-Tue: Embedding generation
• Wed-Thu: Similarity search
• Fri: Cache optimization

Week 16: Integration Testing

• Mon-Wed: End-to-end tests
• Thu-Fri: Performance benchmarking

Week 17: API and CLI

• Mon-Wed: REST API server
• Thu-Fri: CLI tool

Week 18: Documentation and Release

• Mon-Tue: Documentation
• Wed-Thu: Final optimization
• Fri: Release v0.1.0

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Appendix A: Team Roles

Development Team (2-4 people)

Technical Lead (1 person)

• Responsibilities: Architecture, code review, technical decisions
• Time commitment: 100%
• Skills: Rust expert, ML/OCR experience

Core Developer (1-2 people)

• Responsibilities: Implementation, testing, documentation
• Time commitment: 100%
• Skills: Rust, image processing, API development

ML Engineer (1 person, part-time)

• Responsibilities: Model integration, optimization
• Time commitment: 50%
• Skills: ONNX, PyTorch, model optimization

DevOps Engineer (1 person, part-time)

• Responsibilities: CI/CD, deployment, monitoring
• Time commitment: 25%
• Skills: Docker, Kubernetes, GitHub Actions

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Appendix B: Tools and Technologies

Development Tools

• Language: Rust 1.77+
• Build: Cargo, cargo-make
• Testing: cargo test, Criterion.rs
• Profiling: perf, flamegraph, heaptrack
• CI/CD: GitHub Actions
• Documentation: rustdoc, mdBook

Core Libraries

• Image: image, imageproc, fast_image_resize
• ML: tract-onnx, ndarray
• Async: tokio, rayon
• Web: axum, tower
• Serialization: serde, rkyv
• Vector DB: ruvector-core

Infrastructure

• Containerization: Docker
• Orchestration: Kubernetes (optional)
• Monitoring: Prometheus, Grafana
• Logging: tracing, opentelemetry

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Appendix C: Review Checkpoints

Phase Gate Reviews

End of Phase 1 (Week 4):

• Review: Requirements completeness
• Approval: Stakeholder sign-off
• Criteria: 100% requirements documented

End of Phase 2 (Week 6):

• Review: Algorithm design
• Approval: Technical lead
• Criteria: All algorithms pseudocoded

End of Phase 3 (Week 9):

• Review: Architecture implementation
• Approval: Technical lead + team
• Criteria: All crates compile, traits defined

End of Phase 4 (Week 15):

• Review: Core functionality
• Approval: Technical lead + QA
• Criteria: 80%+ test coverage, performance targets met

End of Phase 5 (Week 18):

• Review: Production readiness
• Approval: All stakeholders
• Criteria: All success metrics achieved

Weekly Reviews

• Monday: Sprint planning
• Wednesday: Mid-week check-in
• Friday: Demo and retrospective

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Document History

Version	Date	Author	Changes
1.0.0	2025-11-28	Strategic Planning Agent	Initial roadmap created

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Next Steps

1. Immediate (This Week):

   • Complete Phase 3 trait definitions
   • Set up error handling framework
   • Create configuration system

2. Short-term (Next 2 Weeks):

   • Begin TDD Cycle 1 (Image Preprocessing)
   • Download and prepare test datasets
   • Set up benchmark infrastructure

3. Medium-term (Next Month):

   • Complete TDD Cycles 1-3
   • Achieve Milestone M1 (Basic Image → Text)
   • Begin model integration

4. Long-term (3 Months):

   • Complete all TDD cycles
   • Achieve all milestones M1-M5
   • Release v0.1.0

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For questions or updates to this roadmap, please contact the project lead or open a GitHub issue.